Big data: Data wrangling

Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling

Data Wrangling for Big Data: Challenges and Opportunities

Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield a data set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confront...

Wrangling Big Data Through Diversity, Research Education and Partnerships.

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Wrangling Galaxy’s reference data

UNLABELLED The Galaxy platform has developed into a fully featured collaborative workbench, with goals of inherently capturing provenance to enable reproducible data analysis, and of making it straightforward to run one's own server. However, many Galaxy platform tools rely on the presence of reference data, such as alignment indexes, to function efficiently. Until now, the building of this cac...

Towards Automated Relational Data Wrangling

It is well-known in data science that 80% of the work is devoted to preprocessing and only 20% to the actual machine learning or data mining step. This motivates us to explore different ways to (help) automate that preprocessing step. This note focusses on the question whether it is possible to (help) automate the data wrangling process for tabular data in data science.